TensorFlowGHSA-mhhc-q96p-mfm9

Infinite loop in TFLite

CVE-2021-37686 · Published Aug 25, 2021 · updated Jul 8, 2026

Medium5.5

Fix: upgrade to 2.6.0rc2 or later

Impact

The strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122):

```cc

for (int i = 0; i < effective_dims;) {

if ((1 << i) & op_context->params->ellipsis_mask) {

// ...

int ellipsis_end_idx =

std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count,

effective_dims);

// ...

for (; i < ellipsis_end_idx; ++i) {

// ...

}

continue;

}

// ...

++i;

}

An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop.

Patches

We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695).

The fix will be included in TensorFlow 2.6.0. This is the only affected version.

For more information

Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for...

Affected versions

Package	Affected	Fixed in
tensorflow PyPI	>= 2.6.0rc0, < 2.6.0rc2	2.6.0rc2

Details and references

### Impact The strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122): ```cc for (int i = 0; i < effective_dims;) { if ((1 << i) & op_context->params->ellipsis_mask) { // ... int ellipsis_end_idx = std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count, effective_dims); // ... for (; i < ellipsis_end_idx; ++i) { // ... } continue; } // ... ++i; } ``` An attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop. ### Patches We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695). The fix will be included in TensorFlow 2.6.0. This is the only affected version. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

CVSS 3.1: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Severity from: GitHub (reviewed advisory)
Weakness: CWE-835
Also known as: BIT-tensorflow-2021-37686, CVE-2021-37686, PYSEC-2021-308, PYSEC-2021-599, PYSEC-2021-797

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